Adapting Dynamic Appearance for Robust Visual Tracking

This paper designs a robust discriminative tracking method based on correlation filter and key point matching, the highlight of which is the ability to adapt appearance changes. Toward the complicated and changeable real-world scene, a multi-channel of optimal selection correlation filter (MOSCF) is proposed, and it can model the holistic appearances by learning short-term dynamic features. Meantime, a key point feature set is maintained for representing the long-term stable appearances. In order to combine the similarity evaluation scores of two appearance features, a confidence fusion framework is established to obtain the final output confidence map. Particularly, partially occluded object is located by a kind of local key point voting mechanism. To alleviate error accumulation caused by inappropriate learning, an adaptive learning ratio is set to independently update the filter template corresponding to each channel of MOSCF. The results of the qualitative and quantitative experiments on the OTB-100 benchmark suggest that the proposed tracker outperforms several state-of-the-art methods.